This paper describes an approach to measure the intensity and the territorial dispersion of spatial proximity effects, which affect efficiency scores obtained through data envelopment analysis in the field of municipal waste management systems (MWMSs). In particular, we show that these analyses cannot be conducted by relying on efficiency scores that are not comparable over time and treated as cross-sectional data, as is done in most previous studies. Instead, the use of panel data is a key element to obtain reliable results. We used a meta-frontier approach to obtain meta-efficiency scores comparable over time and a modified conditional autoregressive (CAR) model to provide an estimation of the intensity of spatial proximity effects. This approach was applied to data on 277 MWMSs located in the Italian region of Abruzzo. Our method provides useful information for policymakers. In particular, the areas in which stagnating and suboptimal performance can be expected over time can be identified by plotting over the regional territory the posterior medians of the random effects obtained by the spatial component of the CAR model together with the highly efficient municipalities. To improve efficiency, these areas require an active intervention by levels of government higher than the municipal level.

Spatio-temporal modelling of municipal waste management systems' meta-efficiency scores

Marialisa Mazzocchitti;Alessandro Sarra
2021-01-01

Abstract

This paper describes an approach to measure the intensity and the territorial dispersion of spatial proximity effects, which affect efficiency scores obtained through data envelopment analysis in the field of municipal waste management systems (MWMSs). In particular, we show that these analyses cannot be conducted by relying on efficiency scores that are not comparable over time and treated as cross-sectional data, as is done in most previous studies. Instead, the use of panel data is a key element to obtain reliable results. We used a meta-frontier approach to obtain meta-efficiency scores comparable over time and a modified conditional autoregressive (CAR) model to provide an estimation of the intensity of spatial proximity effects. This approach was applied to data on 277 MWMSs located in the Italian region of Abruzzo. Our method provides useful information for policymakers. In particular, the areas in which stagnating and suboptimal performance can be expected over time can be identified by plotting over the regional territory the posterior medians of the random effects obtained by the spatial component of the CAR model together with the highly efficient municipalities. To improve efficiency, these areas require an active intervention by levels of government higher than the municipal level.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/754683
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